SWITCH provides innovative, unique internet services for the Swiss universities and internet users. SWITCH offers collaboratively developed ICT solutions that empower users in and beyond the academic world to achieve leading edge results in a globally competitive environment. Follow this blog to get articles on OpenStack and related technologies.
At SWITCH we are looking to provide a container platform as a Service solution. We are working on Kubernetes and Openshift to gauge what is possible and how a service could be structured. It would be really nice to use the existing Openstack username and password to authenticate to Kubernetes. We tested this solution and it works great.
How does it work ? Lets start from the client side.
Kubernetes users use the kubectl client to access the cluster. The good news is that since version v1.8.0 of the client, kubectl is able to read the usual openstack env variables, contact keystone to request a token, and forward the request to the kubernetes cluster using the token. This was merged the 7th of August 2017. I could not find anywhere how to correctly configure the client to use this functionality. Finally I wrote some documentation notes HERE.
How does it work on the Kubernetes master side ?
The Kubernetes API receives a request with a keystone token. In the Kubernetes language this is a Bearer Token. To verify the keystone token the Kubernetes API server will use a WebHook. What does it means ? That the Kubernetes API will contact yet another Kubernetes component that is able to authenticate the keystone token.
You can then achieve a “soft multitenancy” where every user belonging to a specific keystone project is limited with permissions to a specific namespace. I talk about soft multitenancy because all the pods from all the namespaces, depending on your networking solution, could end up on the same network with a completely open policy.
I would like to thank Dims and the other people on the Slack channel #sig-openstack for the great help while developing this Kubernetes deployment.
I wrote in the past how to deploy Kubernetes on SWITCHengines (Openstack) using this ansible playbook. When I wrote that article, I did not care about the networking setup, and I used the proposed weavenet plugin. I went to Sydney at the Openstack Summit and I saw the great presentation from Angus Lees. It was the right time to see the presentation because I recently watched this video where they explain the networking of Kubernetes when running on GCE. Going back to Openstack, Angus mentioned that the Kubernetes master can talk to neutron, to inject routes in the tenant router to provide connectivity without NAT among the pods that live in different instances. This would make easier the troubleshooting, and would leave MTU 1500 between the pods.
It looked very easy, just use:
and specify in the cloud config the router uuid.
Our first tests with version 1.7.0 did not work. First of all I had to fix the Kubernetes documentation, because the syntax to specify the router UUID was wrong. Then I had a problem with Security groups disappearing from the instances. After troubleshooting and asking for help on the Kubernetes slack channel, I found out that I was hitting a gophercloud known bug.
The bug was already fixed in gophercloud at the time of my finding, but I learned that Kubernetes freezes an older version of this library in the folder “vendor/github.com/gophercloud/gophercloud”. So the only way to get the updated library version was to upgrade to Kubernetes v1.8.0, or any newer version including this commit.
After a bit of testing every works now. The changes are summarised in this PR, or you can just use the master branch from my git repository.
After you deploy, the K8s master will assign from network ClusterCIDR (usually a /16 address space) a smaller /24 subnet per each Openstack instance. The Pods will get addresses from the subnet assigned to the instance. The kubernetes master will inject static routes to the neutron router, to be able to route packets to the Pods. It will also configure the neutron ports of the instances with the correct allowed_address_pairs value, so that the traffic is not dropped by the Openstack antispoofing rules.
This is what a show of the Openstack router looks like:
We have been running Ceph in production for SWITCHengines since mid-2014, and are at the third generation of servers now.
SWITCHengines storage server evolution
First generation, since March 2014: 2U Dalco based on Intel S2600GZ, 2×E5-2650v2 CPUs, 128GB RAM, 2×200GB Intel DC S3610 SSD, 12×WD SE 4TB
Second generation, since Dec. 2015: 2U Dalco based on Intel S2600WTT, 1×E5-2620v4 CPU, 64GB RAM, 2×200GB Intel DC S3610 SSD, 12×WD SE 4TB
Third generation, since June 2017: 1U Quanta S1Q-1ULH-8, 1×Xeon D-1541 CPU, 64GB RAM, 2×240GB Micron 5100 MAX SSD, 12×HGST Ultrastar He8 (TB)
What all servers have in common: 2×10GE (SFP+ DAC) network connections, redundant power supplies, simple BMC modules connected to separate GigE network.
We run all those servers together in a single large Ceph RADOS cluster (actually we have two clusters in different towns, but for this article I focus on just the larger and more heavily loaded one). The cluster has 480 OSDs, contains about 500TiB user data, mostly RBD block devices used by OpenStack instances, and some S3 object storage, including video streaming directly from RadosGW to browsers. Cluster-wide I/O rates during my measurements were around 2’700 IOPS, 150MB/s read, 65MB/s write. We didn’t apply any particular optimization for energy or otherwise.
To understand the story behind the server types: Initially we used the same server chassis for compute and storage servers. We also used the same relatively generous CPU and RAM configurations. This would have allowed us to turn compute into storage servers (or vice-versa) relatively easily. When purchasing the second server generation we saved some money by reducing CPU power and RAM. For the third generation, we opted for increased density and efficiency made possible by a “system-on-a-chip” (Xeon D)-based server design.
Power measurement results
All these servers have IPMI-accessible power sensors. Last week my colleagues did some measurement with an external power meter, and found that (for the servers they tested—not all types, for lack of time) the values from the IPMI readers are within 5% or so of the values from the “real” power meter. Good enough!
Unfortunately we don’t yet feed IPMI measurements into any of our continuous measurement tools (Carbon/Graphite/Grafana or Nagios). If you do that, please use the comments to tell us how you set this up.
But recently I looked at the IPMI power consumption readings for these servers during a time of relatively light use (weekend) and got the following results:
Gen 1: 248W
Gen 2: 205W
Gen 3: 155W
Note that the Gen 3 servers have larger disks, and thus Ceph puts twice as much data on them, and thus they get double the IOPS than the old servers. Still, they use significantly less power. This is partly due to the simplified mainboard and more modern CPU, partly to the Helium-filled disks that only draw ~4.5W each (when idle) as opposed to ~7.5W for the older 4TB drives.
Cost to power a Terabyte-year of user data
Just for fun, I also performed some cost calculations in relation to usable space, under the following assumptions:
We pay 0.15 €/kWh. (actually I used CHF, but doesn’t really matter—some countries will pay more than this even without overhead, others pay only half, so this would cover some of the other directly energy-dependent costs like A/C and redundancy. Anyway it’s about the relative costs
We can fill disks up to an average of 70% before things get messy.
When using traditional three-way replication, storing a usable Terabyte for a year costs us the following amounts just for power:
Gen 1: € 29.10
Gen 2: € 24.05
Gen 3: € 9.09 (note again that these have twice the capacity)
If we assume Erasure Coding with 50% overhead, e.g. 2+1, then the power cost would go down to
Gen 1: € 14.55
Gen 2: € 12.03
Gen 3: € 4.55
We could consider even more space-efficient EC configurations, but I don’t have any experience with that… “left as an exercise for the reader”.
In conclusion, we could say that advances in hardware (more efficient servers, larger and more efficient disks), software (EC in Ceph), as well as our own optimizations (less spare CPU/RAM) have brought down the power component of our storage costs by a factor of 6.5 over these 3.5 years. Not bad, huh? Of course there are trade-offs: the new servers have lower IOPS per space, EC uses more CPU and disk-read operations etc.
The next frontier: powering down idle disks
Finally, I also took an unused server of the Gen 3 ones (we keep a few powered down until we need them—I powered one on for the test). It consumed 136W, not much less than the 155W under (light) load. Although the 12 8TB disks weren’t even mounted, they were spinning. Putting them all in “standby” mode with sudo hdparm -y lowered the total system load to just 82W. So for infrequently accessed “cold storage”, there’s even more room for optimization—although it might be tricky to leverage standby mode in practice with a system such as Ceph. At least scrubbing strategy would need to be adapted, I guess.
When we built the SWITCHdrive service on the OpenStack platform that was to become SWITCHengines, that platform didn’t really support IPv6 yet. But since Spring 2016 it does. This week, we enabled IPv6 in SWITCHdrive and performed some internal tests. Today around noon, we published its IPv6 address (“AAAA record”) in the DNS. We quickly saw around 5% of accesses use IPv6 instead of IPv4.
In the evening, this percentage climbed to about 14%. This shows the relatively good support for IPv6 on Swiss broadband (home) networks, notably by the good folks at Swisscom.
The lower percentage during office (and lecture, etc.) hours shows that the IPv6 roll-out to higher education campuses still has some way to go. Our SWITCHlan backbone has been running “dual-stack” (IPv4 and IPv6 in parallel) in production for more than 10 years, and most institutions have added IPv6 configuration to their connections to us. But campus networks are wonderfully complex, so getting IPv6 deployed to every network plug and every wireless access point is a daunting task. Some schools are almost there, including some large ones that don’t use SWITCHdrive—yet!?—so the 5% may underestimate the extent of the roll-out for the overall SWITCH community. The others will follow in their footsteps. They can count on the help of the community and benefit from IPv6 training courses organized by our colleagues in the security and network teams. Contact us if you need help!
[Update: After a few weeks, the proportion of IPv6 traffic increased somewhat. Now we typically see around 10% during office hours and 20% during weekends. So the “retail” sector is still clearly ahead of (our academic) enterprise networks in terms of IPv6 penetration.]
Is it really possible with Openstack to start 1000 instances, make a parallel computation, and then save the data and delete the instances ?
To answer this question we tested it on SWITCHengines. I had a lot of troubles getting this work, and I have to thank other Openstack Operators I been chatting with: Mattia Belluco, Matteo Panella and Anton Aksola.
Our Openstack control plane is deployed with a dedicated pet VM for each Openstack service (Nova, Cinder, Neutron, Glance and Keystone) and a generic controller VM where we run the mysql and the rabbitmq services. This configuration makes possible to monitor each Openstack service as an isolated VM, and it makes easier for us to identify bottlenecks in the control plane.
For this experiment we never used the web interface, but the Openstack CLI with this reference command line.
The c1.small flavor has just 1 CPU core and 1GB of RAM.
We did the experiment in 4 steps, trying with 100, 200, 400 and 1000 instances. To make sure that the instances were really started and operational, we used cloud-init to make them phone home to a registration server. This is a very easy cloud-init feature to use, here is an example cloud-init.txt file:
The first test with 100 instances did not work. We tried a few runs and we always had a minimum of 4 to a maximum of 7 instances that did not start for various reasons. Monitoring our control plane we noticed that we were saturating the CPUs and memory of the nova and neutron pets.
We increased the resources for both the nova and the neutron pets from 4 to 16 CPU cores and we doubled the memory from 8 GB to 16 GB.
After these changes we were able to start 100 instances without problems. We noticed that the neutron pet had an higher load than the nova pet during the process of creating 100 instances.
When we tried with 200 instances, those were all reported as Running by Openstack but we always had a minimum of 8 to a maximum of 20 instances not phoning home. Looking at the serial console with the command:
openstack console log show
we noticed that these instances were not able to get an IP address from the DHCP server, and the DHCP client would give up after 300 seconds. Using the hint that the neutron pet was more loaded than the nova pet, we found out that the nova instances reached the RUNNING state while the corresponding neutron ports were still in the BUILDING phase.
Thinking of a race condition between nova instances and neutron ports, I asked on the Openstack Developers mailing list, and it turned out that we had a wrong configuration.
After fixing the configuration we had the same result, but instead of the instances starting and not being able to obtain an IP address, they never started and were reported in ERROR state by Openstack.
The real challenge was not to schedule 200 instances, but to allocate 200 network ports.
Troubleshooting in this direction we observed that the rabbitmq queues of the neutron dhcp agents were filling up during the ports creation. For each created port the dhcp agent had to add a corresponding line to the file /var/lib/neutron/dhcp/$UUID/host. Where $UUID is the corresponding Neutron network UUID.
We looked into the detail of what happens when a neutron port is created. Using the guru meditation report we traced down the culprit in a slow “ip route list” call.
This command is called everytime a neutron port is created:
time sudo ip netns exec qdhcp-7a1cfb7f-2960-45f5-903f-0d602450525a ip route list
default via 10.10.0.1 dev tapaf136b11-a5
10.10.0.0/16 dev tapaf136b11-a5 proto kernel scope link src 10.10.0.2
However calling the same command within neutron-rootwrap takes about 10 times longer:
time sudo neutron-rootwrap /etc/neutron/rootwrap.conf ip netns exec qdhcp-7a1cfb7f-2960-45f5-903f-0d602450525a ip route list dev tapaf136b11-a5
default via 10.10.0.1
10.10.0.0/16 proto kernel scope link src 10.10.0.2
Once we identified this bottleneck, we changed the configuration again to enable the Openstack rootwrap to work in daemon mode.
We had to change the agent section of neutron.conf
After this change, we were able to start successfully 200, 400 and 1000 instances.
With 1000 instances we still get a HTTP 504 gateway timeout error.
This is because the nova-api server takes longer than the reverse proxy timeout to answer the request. The reverse proxy replies with HTTP 504 but the nova-api server will later finish to process the request with a HTTP 200. This is easily fixed using a longer timeout, but we plan to trace the problem in detail to shorten the processing time of the request.
Finally the answer is yes, with Openstack it is really possible to start 1000 instances quickly to have compute power just when needed.
TL;DR: A small percentage of VMs running in the Zurich region of SWITCHengines weren’t protected by the default firewall from 11.8 to 18.8.2017. The root problem has been fixed. We have implemented additional measures to prevent this from happening again.
Thanks to one of our users, we recently were made aware of a security problem that has affected a small percentage of our customers virtual machines. While it looked like the machines were protected by the standard OpenStack firewall rules, in effect those machines were completely open to the Internet. We were able to fix the problem within a few hours. Our investigations showed that the problem was existent for roughly one week (starting 11.8.2018, ending 18.8.2017) due to a mismatch between the software deployed on 5 specific hypervisors and the configuration applied to them.
If you were affected by this problem, you already have received an email from us. If you didn’t receive anything, your VMs were secure.
Each VM running on SWITCHengines is completely isolated from the Internet. VMs run on a number of hypervisors (physical servers) that share an internal physical network (The hypervisors also are isolated from the Internet and can’t be reached from the outside). In order for a VM to reach the Internet, it has to use a software defined virtual network that connects it to one of our “Network Nodes”. These network nodes are special virtual machines that are have both an address on our private network and and address on the Internet – and can thus bridge between virtual machines and the Internet. They are using a technique called NAT (Network Address Translation) to provide external access to the VMs and vice versa. The software component running on the network nodes is called Neutron – it’s a part of the OpenStack project.
On each hypervisor, another part of Neutron runs. This part controls the connectivity and the security access to the virtual machines running on the hypervisor. The neutron component on the hypervisor is responsible for managing the virtual networks and the access from the VMs to this virtual network. In addition it also controls the Firewall component (we use `iptables` a standard component of the Linux operating system running on the hypervisors). By default, each VM running on the hypervisor is protected by strict security rules that disallow access to any ports on the VM.
A user can configure these security groups by adding rules through the SWITCHengines GUI. When a security rule is modified, the Neutron Server sends a command to the Neutron component on the Hypervisor that in turn adds the relevant rules to the `iptables` configuration on that specific hypervisor.
Cause of loss of Firewall functionality
Besides upgrading the OpenStack software regularly (every 6 months) we also maintain and upgrade the operating system on the hypervisors. The last months we have been busy upgrading the hypervisors from Ubuntu Trusty (14.04) to Ubuntu Xenial (16.04). Upgrading a server takes a long time, because we live migrate all running VMs from that server to another, then upgrade the server OS (together with upgrades of any installed packages) and then take the server back into production – i.e. move VMs to it. This process has been ongoing for the last 3 months and we will be finished in September 2017.
The 5 hypervisors that were affected by the problem, were the first ones to be upgraded to Ubuntu Xenial and the OpenStack Newton components. Because they were upgraded early, they had an older version of OpenStack Newton installed. There was a bug in the Neutron component of that OpenStack release – however, that bug didn’t surface at first.
On the 11. August, we did routine configuration changes to all hypervisors running on the Newton release. This config change went well on all recent installed hypervisors, but caused the Firewall rules to be dropped on the older machines.
When we were made aware of the problem and upgraded the Newton component, the firewall rules were recreated and the VMs protected.
We have identified two fundamental problems through this incident:
Some servers have different software versions of the same components due to them being installed at different times
We didn’t detect the lack of firewall rules for the affected VMs
To address the first problem, we are being more strict about specifying the exact release of each software component that we install on all our servers. We strive to have identical installations everywhere.
To address the second problem, we have written a script that checks the firewall status for all running virtual machines. We will incorporate this script into our regular monitoring and testing so that we will be alerted about that problem automatically, should it happen again.
We take the security of SWITCHengines serious and we are sorry that we left some of our customers VMs unprotected. Thanks to the people reporting the problem and thank you for your understanding. We are sorry for any problems this might have caused you.
Increasing demand for container orchestration tools is coming from our users. Kubernetes has currently a lot of hype, and often it comes the question if we are providing a Kubernetes cluster at SWITCH.
At the moment we suggest that our users deploy their own Kubernetes cluster on top of SWITCHengines. To make sure our Openstack deployment works with this solution we tried ourself.
The first problem I discovered deploying Kubernetes is the total lack of support for IPv6. Because instances in SWITCHengines get IPv6 addresses by default, I run into problems running the playbook and nothing was working. The first thing you should do is to create your own tenant network with a router, with IPv4 only connectivity. This is already explained in detail in our standard documentation.
Because the ansible playbook creates instances through the Openstack API, you will have to source your Openstack configuration file. We extend a little bit the usual configuration file with more variables that are specific to this ansible playbook. Lets see a template:
Lets review what changes. It is important to add also the variable OS_PROJECT_ID because the Kubernetes code that creates Load Balancers requires this value, and it is not able to extract it from the project name. To find the uuid just use the Openstack cli:
openstack project show myprojectname -f value -c id
The KEY is the name of an existing keypair that will be used to start the instances. The IMAGE is also self explicative, at the moment only Xenial is tested by me. The variable NETWORK is the name of the tenant network you created earlier. When you created a network you created also a subnet, and you need to set the uuid into SUBNET_UUID. The last variable is FLOATING_IP_NETWORK_UUID that tells kubernetes the network where to get floating IPs. In SWITCHengines this network is always called public, so you can extract the uuid like this:
openstack network show public -f value -c id
You can customize your configuration even more, reading the README file in the git repository you will find more options like the flavors to use or the cluster size. When your configuration file is ready you can run the playbook:
It will take a few minutes to go through all the tasks. When everything is done you can ssh into the kubernetes master instance and check that everything is running as expected:
ubuntu@k8s-master:~$ kubectl get nodes
NAME STATUS AGE VERSION
k8s-1 Ready 2d v1.6.2
k8s-2 Ready 2d v1.6.2
k8s-3 Ready 2d v1.6.2
k8s-master Ready 2d v1.6.2
I found very useful adding bash completion for kubectl:
source <(kubectl completion bash)
Lets deploy an instance of nginx to test if everything works:
kubectl run my-nginx --image=nginx --replicas=2 --port=80
This will create two containers with nginx. You can monitor the progress with the commands:
kubectl get pods
kubectl get events
At this stage you have your containers running but the service is still not accessible from the outside. One option is to use the Openstack LBaaS to expose it, you can do it with this command:
Following this blog post you should be able to deploy Kubernetes on Openstack to understand how things work. For a real deployment you might want to make some customisations, we encourage you to share any patch to the ansible playbook with github pull requests.
Please note that Kubernetes is not bug free. When you will delete your deployment you might find this bug where Kubernetes is not able to delete correctly the load balancer. Hopefully this is fixed by the time you read this blog post.
SWITCH offers a cloud computing service called SWITCHengines, using OpenStack and Ceph. These computing resources are targeted to research usage, where the demand for Big Data (Hadoop, Spark) workloads is increasing. Big Data Analysis requires access to large datasets. In several use cases the analysis is done against public available datasets. To add value to the cloud computing service our plan is to provide a place for researchers to store their data sets and make them available to others. A possible alternative at the moment is Amazon EC2, because many datasets are available for free in Amazon S3 when the computation is done within the Amazon infrastructure. Storing as many datasets as Amazon does is challenging because the real data utilisation in a object storage system is usually 3 times the real data. Each dataset has a size of 100s of TB, so storing a few of them with a 3 times replica factor means working in the PB domain.
While it sounds a easy task to share some scientific data in a cloud datacenter, there are some problems you will have to address.
1) The size of the datasets is challenging. It will cost money to host the datasets. To make the service cost sustainable you must find reduced redundancy solutions, that have a reasonable cost and a reasonable risk of data loss. This is acceptable because there datasets are public and most likely there are going to be other providers hosting a copy, so in case of disaster data recovery will be possible. The key idea is to store the dataset locally to speed up access to the data during computation, not to store the dataset for persistent storage. Before we can host a dataset, we have to download it from another source. Also this first copy operation is challenging because of the size of the data involved.
2) We need a proper ACL system to control who can access the data. We can’t just publish all the datasets with public access. Some datasets require the user to sign an End User Agreement.
How we store the data
We use Ceph in our cloud datacenter as storage backend. We have Ceph pools for the OpenStack rbd volumes, but we also use the rados gateway to provide an object storage service, accessible via S3 and Swift compatible APIs. We believe that the best solution to access the Scientific Datasets is to store them on Object Storage, because the size of the data requires a technical solution that can scale horizontally spreading the data on many heterogeneous disks. Our Ceph cluster works with a replication factor of 3. This means that each object exists in 3 replicas on three different servers. To work with big amounts of data we deployed in production Erasure Coded Pools in our Ceph cluster. In this case at the cost of an higher CPU computation on the storage nodes, is possible to store the data with an expansion for 1.5 instead of 3. We found very useful the blog post cephnotes that explains how to create a new Ceph pool with Erasure Coding to be used with the rados gateway. We are now running this setup in production, and when we create a S3 bucket we can decide if the bucket will land on the normal pool or on the erasure coded pool. To make the decision we use the bucket-location command line option like in this this example:
It is important to note that the bucket-location option should be there when creating the S3 bucket. When writing objects at a later time, the objects will always go to the right pool.
How we download the first copy
How do you download a dataset of 200TB from an external source ? How long does it take ? The task if of course not trivial, to address this problem Amazon started his snowball service. The key idea is that data is shipped to you via snail mail on a physical device. Because we are a NREN, we decided to download the datasets over the GEANT network from other institutions that are hosting datasets. We used rclone for our download operations. It is a modern object storage client written in go, that supports multiple protocols. Working with big amount of data we soon found some issues with the client. The community behind rclone was very active and helped us. “Fail to copy files larger than 80GB” was the most severe problem we had. We also improved the documentation about Swift keys with slashes do not work with Ceph in swift emulation mode.
We also had some issues with the rados gateway. When we write the data, this goes into a Ceph bucket. In theory the data in the bucket can be written and read using both the S3 and the Swift APIs that the rados gateway implements. In reality we found out that there is a difference in how the checksums for the multipart objects is computed when using the S3 or the Swift API. This means that if wrote a object, you have to read it back with the same API you used for writing, otherwise your client will warn you that the object is corrupted. We notified the Ceph developers about this problem opening a bug that is still open.
Set the ACL to the dataset
When we store a dataset we would like to have read-write access for the cloud admin accounts, and read-only access for the users that are allowed to read the data. This means that after the dataset is uploaded, we should be able to easily grant and revoke access to users and projects. S3 and Swift manage the ACL in very different ways. In S3 the concept of inheritance of the ACL from a parent bucket does not exist. This means that for each individual object a specific ACL must be set. We read this in the official documentation:
Bucket and object permissions are completely independent; an object does not inherit the permissions from its bucket. For example, if you create a bucket and grant write access to another user, you will not be able to access that user’s objects unless the user explicitly grants you access.
This does not scale with the size of the dataset because making an API call for each object is really time consuming. There is a workaround, that it is to mark the bucket as completely public, bypassing the authorization for all objects. This workaround does not match with our use case where we have some users with read-only permission but the dataset is not public. As a viable workaround we create for each dataset a dedicated OpenStack tenant ‘Dataset X readonly access’ and what we do is adding and removing users from this special tenant, because this is a fast operation. It becomes very important to set the correct ACL when writing the objects for the first time. Unfortunately our favourite client rclone had no existent support for setting ACLs, so we had to use s3cmd.
In OpenStack Swift you can set the ACL for a Swift container, and all the objects in the container will inherit that property. This sounds very good, but we are not running a real OpenStack Swift deployment, we serve objects in Ceph using an implementation of the Swift api.
During our tests we found out that the rados gateway Swift API implementation is not complete: ACL are not implemented. Other openstack operators reported that native Swift deployments work well with ACL and inheritance of ACL to the objects. However, when touching ACL and large objects also Swift is not bug free.
Access the dataset
To be able to compute the data without wasting time copying the data from the object store to a HDFS deployment, the best is to use Hadoop directly attached to the object store, in what is called the streaming mode. The result of the computation can be stored as well on the object store, or on a smaller HDFS cluster. To get started we published this tutorial.
Unfortunately the latest versions of Hadoop require the S3 backend to support the AWS4 signature, that will not work immediately with your SWITCHengines credentials because of this bug triggered when using Keystone together with the rados gateway. As a workaround we manually have to add the user on our rados gateway deployment, to make the AWS4 signature work correctly.
Making available large scientific datasets on a multi tenant cloud is still challenging but not impossible. Bring the data close to the compute power is of great importance. Letting the user compute public scientific datasets makes a scientific cloud service more attractive.
Moreoever, we envision users producing scientific datasets, and being able to share them with other researchers for further analysis.
Ken D’Ambrosio writes:
> Hey, all. I have a Liberty cloud, and decided for the heck of it to
> start dipping my toe into IPv6. I do have some confusion, however. I
> can choose between SLAAC, DHCPv6 stateful and DHCPv6 stateless — and
> I see some writeups on what they do, but I don’t understand what
> differentiates them. As far as I can tell, they all do pretty much
> the same thing, just with different pieces doing different things.
> E.g., the chart, found here
> — page down a little) shows those three options, but it isn’t clear:
> * How to configure the elements involved
> * What they exactly do (e.g., “optional info”? What’s that?)
> * Why there even *are* different choices. Do they offer functionally
> different results?
SLAAC and DHCPv6-stateless use the same mechanism (SLAAC) to provide instances with IPv6 addresses. The only difference between them is that with DHCPv6-stateless, the instance can also use DHCPv6 requests to get other (than its own address) information such as nameserver addresses etc. So between SLAAC and DHCPv6-stateless, I would always prefer DHCPv6-stateless—it’s a strict superset in terms of functionality, and I don’t see any particular risks associated with it.
DHCPv6-stateful is a different beast: It will use DHCPv6 to give an instance its IPv6 address. DHCPv6 actually fits OpenStack’s model better than SLAAC.
Why DHCPv6-Stateful Fits OpenStack Better
OpenStack (Nova) sees it as part of its job to control the IP address(es) that an instance uses. In IPv4 it uses DHCP (always did). DHCP assigns complete addresses—which are under control of OpenStack. In IPv6, stateful DHCPv6 would be the equivalent.
SLAAC is different in that the node (instance) actually chooses its address based on information it gets from the router. The most common method is that the node uses an “EUI-64” address as the local part (host ID) of the address. The EUI-64 is derived from the MAC address by a fixed algorithm. This can work with OpenStack because OpenStack controls the MAC addresses too, and can thus “guess” what IPv6 address an instance will auto-configure on a given network. You see how this is a little less straightforward than OpenStack simply telling the instance what IPv6 address it should use.
In practice, OpenStack’s guessing fails when an instance uses other methods to get the local part, for example “privacy addresses” according to RFC 4941. These will lead to conflicts with OpenStack’s built-in anti-spoofing filters. So such mechanisms need to be disabled when SLAAC is used under OpenStack (including under “DHCPv6-stateless”).
Why we Use SLAAC/DHCPv6-Stateless Anyway
Unfortunately, most GNU/Linux distributions don’t support Stateful DHCPv6 “out of the box” today.